Field of the Invention
The present invention is related to systems, methods, and computer program products for relationship discovery, and more particularly to a system, method, and computer program product of discovering relationships among items such as music tracks, and making recommendations based on user preferences and discovered relationships.
Description of the Background Art
In many applications for the presentation and marketing of online content, personalization of the user's experience is desirable. Knowledge and application of user preferences permit online advertisers to more efficiently target their advertisements to those users who are more likely to respond. Electronic commerce sites are able to suggest products and services that are likely to be of interest to particular users, based on user profiles and preferences. Such suggestions may be made, for example, by sending e-mail to the user, or by presenting a list of recommended items in the context of a dynamically generated web page. Additional applications exist for such functionality, including both online applications (such as personalized radio stations, news delivery, and the like) and non-online applications (such as targeting of direct mail advertising, supermarket checkout coupons, and the like).
One particular application in which user-specific recommendations may be generated is personalized online radio stations. It is known to provide web pages for delivering selected music tracks to individual users, based on user selection. Compressed, digitized audio data is delivered to users in a streaming format (or alternatively in downloadable format), for playback at users' computers using conventional digital audio playback technology such as the Windows Media Player from Microsoft Corporation, or the RealPlayer from Real Networks. It would be desirable for such radio stations to be able to determine which music tracks are likely to be enjoyed by a particular user, even in the absence of, or as a supplement to, explicit selection of particular tracks by the user.
It is desirable, then, to provide accurate methods and systems for discovering user preferences in particular domains and with respect to particular types of products and services. Several prior art techniques exist for discovering user preferences. In one such technique, as described in U.S. Pat. No. 6,064,980, Jacobi et al., “System and Methods for Collaborative Recommendations,” issued May 16, 2000, collaborative filtering is employed. Users are asked to complete an online questionnaire specifying their preferences. Such a questionnaire may be presented to the user, for example, when he or she attempts to register for an online service or purchase an online product. The user's responses may then be stored as a user “profile” in a back-end database. The system correlates the profile to the profiles of other users in order to identify users having similar tastes; recommendations are then generated based on the preferences of the similar users.
However, many users may be reluctant to complete such online questionnaires, due to privacy concerns, or due to an unwillingness to take the time required to answer the questions. Furthermore, such questionnaires often fail to accurately collect user preference information, since they do not actually reflect the user's consumptive behavior; in other words, users may answer inaccurately because they are unaware of (or dishonest about) their own preferences. In addition, the accuracy of the results is limited by the quality of the designed questions. Finally, the stored user profile merely provides a description of the user's preferences at the particular point in time when the questionnaire was completed, and may fail to take into account subsequent changes and/or refinements to the preferences.
A second prior art technique for discovering user preferences is to observe user behavior. In online commerce environments, user behavior can be observed by tracking the particular pages visited, products ordered, files downloaded or accessed, and the like. Users may be prompted for login identifiers, providing a mechanism for identifying users. In addition to or instead of login, cookies may be stored on users' computers, as is known in the art, in order to recognize a user who has previously visited a site. Thus, user behavior can be tracked over multiple visits, without requiring the user to set up a login identifier or to even be aware that his or her behavior is being tracked.
For example, many online commerce sites keep track of user purchases, and, based on such purchases, make recommendations as to products and services that are likely to be of interest to a particular user. Such recommendations may be based on analysis of the purchases of other users who have purchased the same products and services. User browsing may also be monitored, so that recommendations may be based on products that the user has browsed, as well as those he or she has purchased.
The above-described technique for observing user behavior may lead to inaccurate results. Relatively few data points may be available, particularly when recommendations are based on user purchases. For example, a typical user may make four or five purchases annually from any particular online store, and may distribute his or her purchases among several stores, including online, conventional retail, and/or other outlets. The relatively small number of purchases tracked by any particular store may be insufficient to develop a reasonably accurate user profile in a relatively short period of time. Thus, recommendations in such systems are often inaccurate since they are based on insufficient information.
Furthermore, some purchases may be gifts, and may thus fail to accurately reflect personal preferences of the purchaser. In some cases, the purchaser may specify that an item is a gift (by requesting gift-wrapping, or a gift message for example), so that the item may be excluded from user behavior analysis; however in many cases the purchaser may not make the online merchant aware of the fact that the purchase is a gift, and there may be no way for the merchant to make this determination. Distortions and inaccuracies in the user profile may then result. In particular, when relatively few data points are available, each individual gift purchase may have a particularly powerful distorting effect on the user profile.
Finally, distortions may result from the fact that, once a purchase is made, the merchant may not be able to easily determine whether the purchaser was satisfied with the product. This is a particular problem in connection with products that are typically only purchased once, such as books, videos, and compact discs. A user may purchase a compact disc and listen to it only once, finding the music not to his liking. The user may purchase a second compact disc, by another artist, and enjoy it immensely, listening to it hundreds of times. The user's behavior with respect to the online merchant is the same for the two cases—namely, a single purchase of a compact disc. The online merchant cannot determine, from the purchasing behavior, the musical tastes and preferences of the user, since the merchant is not aware of the post-purchase behavior of the user.
In addition to the above problems with data gathering for developing user profiles, there are additional limitations and shortcomings of conventional recommendation engines, with respect to the data analysis that is performed to generate recommendations. Conventionally, recommendations are made based on data analysis performed on the observed user behavior. Several types of data analysis are known in the art for developing recommendations based on observed behavior. One commonly used technique is to observe that people who buy a particular product X also tend to be more likely to buy a particular product Y. Thus, the system may suggest, to a user who is observed purchasing (or browsing) product X, that he or she may also be interested in product Y. The basis for the suggestion is an observed correlation between purchasers of product X and purchasers of product Y.
Such a data analysis technique often leads to inaccurate results, particularly when the observed purchase is a relatively rare product. Relationships among such products often tend to be overstated, since relatively few data points are available for both the purchased product and the suggested product. Thus, the significance of a particular co-occurrence (i.e. an observed purchase of two products by the same individual) is given undue weight, when in actuality the co-occurrence may merely be a coincidence and may not provide an accurate indication of a relationship between the two products. In addition, certain products, such as “best sellers,” tend to appeal to virtually all consumers, so that co-occurrence is seen between a best seller and nearly every other product. Conventional data analysis techniques often fail to yield meaningful results, because of both the overstated significance of coincidental co-occurrence, and the overpowering influence of best sellers.
The following is an illustration of the deficiencies of conventional data analysis techniques in situations involving a rare product and/or best sellers. Analysis of the co-occurrence of events A and B (e.g. a purchase of product A and a purchase of product B) involves construction of the following matrix:
                    B                                      ~          B                      ⁢                                                        A                                                      ~                A                                                                                                                                              k                  ⁡                                      (                    AB                    )                                                                                                k                  ⁡                                      (                                          ~                      AB                                        )                                                                                                                        k                  ⁡                                      (                                          A                      ~                      B                                        )                                                                                                k                  ⁡                                      (                                          ~                      A                      ~                      B                                        )                                                                                                                                            k                  ⁡                                      (                    A                    )                                                                                                k                  ⁡                                      (                                          ~                      A                                        )                                                                                            ⁢                                                                                                                                                                                                                                                                      k                      ⁡                                              (                        B                        )                                                                                                                                                                    k                ⁡                                  (                                      ~                    B                                    )                                                                                                  k          ⁢                      (            *                    )                    
where:
k(AB) is a count of the number of times A and B both occurred;
k(˜AB) is a count of the number of times A did not occur and B occurred;
k(A˜B) is a count of the number of times A occurred and B did not occur;
k(˜A˜B) is a count of the number of times neither A nor B occurred;
k(A) is a count of the total number of times A occurred;
k(˜A) is a count of the total number of times A did not occur;
k(B) is a count of the total number of times B occurred;
k(˜B) is a count of the total number of times B did not occur; and
k(*) is a count of the total number of events.
If p(B|A)=p(B), where p(B|A) is the probability of B given that A has occurred, and p(B) is the probability of B, then events A and B are considered to be independent. It also follows that if p(A)p(B)=p(AB), where p(A) is the probability of A, p(B) is the probability of B, and p(AB) is the probability of both A and B occurring, then A and B are independent.
It is assumed that probabilities can be estimated from observed event occurrences using the maximum likelihood principle, so that
                              k          ⁡                      (            AB            )                                    k          ⁡                      (            A            )                              ≅              p        ⁡                  (                      B            |            A                    )                      ;    and                      k        ⁡                  (          B          )                            k        ⁢                              (                                                          *                )              ≅          p      ⁡              (        B        )            
As discussed above, A and B are independent if p(B|A)=p(B). Accordingly, if
                    p        ⁡                  (                      B            |            A                    )                            p        ⁡                  (          B          )                      >    1    ,A and B are appearing together more than expected for independent events. Substitution of the above equations yields the following test:
If
                                          k            ⁡                          (              AB              )                                ⁢          k          ⁢                                    (                                                                    *                          )                              k          ⁡                      (            A            )                          ⁢                  k          ⁡                      (            B            )                                >    1    ,a co-occurrence relationship can be established.
The above-described technique is deficient, in that quantization effects tend to overpower meaningful results. Particularly where event counts are small, coincidences often translate into perfect correlations, yielding misleading results.
Pearson's Chi-Squared test improves on the above-described technique by introducing an estimate of significance. According to this technique, independence is assumed and a determination of how many k(AB) and k(A˜B) would be expected. Expected k(AB) can be expressed as:
            k      ^        ⁡          (      AB      )        =                    k        ⁡                  (          A          )                    ⁢              k        ⁡                  (          B          )                                    k        ⁢                              (                                                          *                    )      
If {circumflex over (k)}(AB) and all similar estimates are greater than five, the distribution of the count: of multinomially distributed events can be approximated using a normal distribution. Assuming a normal distribution, the difference between the observed k(AB) and the expected value can be determined and squared. The sum of the squared normal distribution is known to be χ2. Accordingly, the significance of the difference is then determined, and unexpected co-occurrence defined.
However, Pearson's Chi-Squared test yields misleading results when one of the events is relatively rare (such as when the expected count is less than 5). In such situations, the assumption of normal distribution tends to lead to an overstatement of the significance of the co-occurrence.
A second prior art data analysis technique for developing product recommendations employs archetypal customers in order to categorize users according to observed behavior. Such techniques are employed, for example, in LikeMinds 3.1 from Macromedia Corporation. A set of customers is selected and denoted the archetype set. Prospective purchasers and users are compared with the archetype set in order to determine which archetypes they most resemble. However, such systems may also lead to inaccurate results, since the set of archetypes is often insufficient to accurately describe individual real-world users. In many situations, archetypes are non-orthogonal to one another, and the archetype set thus provides a poor basis space for modeling users. The system may thus fail to provide a concise description of a user (if too many archetypes are needed to provide an accurate description), or the description may not be accurate (if too few archetypes are used).
In some variations, users may be presented with a list of archetypes and asked to select which archetype(s) they most resemble. Such an approach leads to similar disadvantages as described above with respect to questionnaires, and also may lead to inaccuracies as users have difficulty selecting a subset of archetypes that accurately reflects their own preferences. In such an approach, it rapidly becomes apparent that, no matter how many archetypes are available, the user cannot easily be defined as a sum of fixed archetypes.
The archetype approach also tends to yield recommendations that are dominated by a particular subgroup. Very popular items filter to the top of the list, since most archetypes are readers of bestsellers (as is most everyone; hence the definition of “bestseller”). This massive overlap of best sellers exacerbates the problem of non-orthogonality of the archetype set. If bestsellers are removed from the set of items, results may be inaccurate because coincidental co-occurrences then dominate, as described above. This problem may be even more prevalent when this approach is employed, since the non-orthogonality of the archetype set tends to increase the noise sensitivity of the system, so that coincidental matches (as described above) become even more significant, leading to increased levels of distortion and unsatisfactory results.
Caid et al., U.S. Pat. No. 5,619,709, for “System and method of context vector generation and retrieval” describes an approach that attempts to deal with this problem of non-orthogonality by explicitly constructing an orthogonal basis space with relatively low dimensionality. However, such reduced-dimensionality systems suffer from the limitation that distinctions between words tend to be lost when reducing the dimensionality of the system. The loss of such distinctions can improve recall in an information retrieval system, but leads to a decrease in precision. Precision, expressed as the fraction of high scoring results that are correct, is the most useful figure of merit for a recommendation system.
What is needed is a system and method of generating and providing recommendations to users that avoids the above-described limitations and disadvantages. What is further needed is a system and method of discovering relationships among items, that is not obtrusive to users and that leads to accurate recommendations based on user preferences. What is further needed is a recommendation engine that provides improved accuracy by reacting to user preferences that may change with time, and by collecting a larger number of data points so that more accurate profiles may be developed.